Chapter 1: Understanding and Applying Regression Analysis – Theory as Well as Practice
Chapter 2: Basic Matrix Algebra for Regression Analysis
Chapter 3: Ordinary Least Squares Regression Derived, and Initial Tenets of Estimation Practice Introduced
Chapter 4: Moving from Ordinary to Generalized Least Squares, Illustrated through the Problem of Heteroskedasticity
Chapter 5: Autocorrelated Errors – A Further Look at Generalized Least Squares
Chapter 6: Finding Unusual Cases in Your Data Set – They Aren’t Just 'Outliers'
Chapter 7: Collinearity – Finding and Coping with Very High Correlations Among Explanatory Variables
Chapter 8: Model Specification – How Can We Know When a Model is Good, or Better than a Competing Model?
Chapter 9: Measurement Error in Our Independent and Dependent Variables – How Might This Compromise Your Parameter Estimates, And What Can You Do About It?
Chapter 10: Regression Analysis Is the Gateway - Some Directions for Further Study in Data Science